A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Modélisation de répartition d’espèces aviaires et de feux en forêt boréale du Québec dans un contexte de changement climatique

Les changements climatiques prennent une importance grandissante dans l’étude des phénomènes spatiaux à grande échelle. Plusieurs experts affirment que les changements climatiques seront un des principaux moteurs de changement écologique dans les prochaines décennies et que leurs conséquences seront inévitables. Ces changements se manifesteront sur le milieu physique par la fonte des calottes glaciaires, le dégel du pergélisol, l’instabilité des versants montagneux en zone de pergélisol, l’augmentation de l’intensité, de la sévérité et de la fréquence des événements climatiques extrêmes tels les feux de forêt. Les changements climatiques se manifesteront aussi sur le milieu biologique, tel la modification de la durée de la saison végétative, l’augmentation des espèces exotiques invasives et les changements dans la distribution en espèces vivantes. Deux aspects sont couverts par cette étude : 1) les changements dans la répartition spatiale de 39 espèces d’oiseaux et 2) les modifications dans les patrons spatiaux des feux, en forêt boréale québécoise, tous deux dans l’horizon climatique de 2100. Une approche de modélisation statistique démontre que la répartition spatiale des oiseaux de la forêt boréale est fortement liée à des variables bioclimatiques (R2adj = 0.53). Ces résultats permettent d’effectuer des modélisations bioclimatiques pour le gros-bec errant et la mésange à tête noire quivoient une augmentation de la limite nordique de distribution de l’espèce suivant l’intensité du réchauffement climatique. Finalement, une modélisation spatialement explicite par automate cellulaire permet de démontrer comment les changements climatiques induiront une augmentation dans la fréquence de feux de forêt et dans la superficie brûlée en forêt boréale du Québec.Climate is getting more important in the study of broad-scale spatial phenomena. Experts agree on the fact that climate change will likely be one of the main drivers of ecological change in the upcoming decade and that its consequences are inevitable. These chances induce consequence on the physical aspects, by ice caps melting, permafrost thawing, slope and soil instability in mountainous permafrost areas and increase of intensity, severity and frequency of extreme weather events such as wildland fires. Moreover, climate chance also causes impacts on the biological aspects, with modification in the growing season, increase in exotic invasive species, and changes in spatial distribution of butterfly and bird species. Two of these aspects are covered in this study: 1) changes in the spatial distribution of 39 bird species and 2) modifications in spatial patterns of wildland fires, in the boreal forest of Quebec, up to year 2100. A statistical modelling approach showed that the spatial distribution of boreal birds is strongly linked to bioclimatic variables (53%). These results enables the bioclimatic modelling of two bird species, the evening grosbeak and black-capped chickadee. In both cases, the model shows an increase of the northernmost limit of distribution of each species following the rate of climate change. Finally, a spatially-explicit cellular automata model showed that climate change will likely induce an increase in wildfire frequency and total area burned for the boreal forest of Quebec

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces

Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata

Recent research techniques, such as genetic algorithm (GA), Petri net (PN), and cellular automata (CA) have been applied in a number of studies. However, their capability and performance in land-cover land-use (LCLU) classification, change detection, and predictive modeling have not been well understood. This study seeks to address the following questions: 1) How do genetic parameters impact the accuracy of GA-based LCLU classification; 2) How do image parameters impact the accuracy of GA-based LCLU classification; 3) Is GA-based LCLU classification more accurate than the maximum likelihood classifier (MLC), iterative self-organizing data analysis technique (ISODATA), and the hybrid approach; 4) How do genetic parameters impact Petri Net-based LCLU change detection; and 5) How do cellular automata components impact the accuracy of LCLU predictive modeling. The study area, namely the Tickfaw River watershed (711mi²), is located in southeast Louisiana and southwest Mississippi. The major datasets include time-series Landsat TM / ETM images and Digital Orthophoto Quarter Quadrangles (DOQQ’s). LCLU classification was conducted by using the GA, MLC, ISODATA, and Hybrid approach. The LCLU change was modeled by using genetic PN-based process mining technique. The process models were interpreted and input to a CA for predicting future LCLU. The major findings include: 1) GA-based LCLU classification is more accurate than the traditional approaches; 2) When genetic parameters, image parameters, or CA components are configured improperly, the accuracy of LCLU classification, the coverage of LCLU change process model, and/or the accuracy of LCLU predictive modeling will be low; 3) For GA-based LCLU classification, the recommended configuration of genetic / image parameters is generation 2000-5000, population 1000, crossover rate 69%-99%, mutation rate 0.1%-0.5%, generation gap 25%-50%, data layers 16-20, training / testing data size 10000-20000 / 5000-10000, and spatial resolution 30m-60m; 4) For genetic Petri nets-based LCLU change detection, the recommended configuration of genetic parameters is generation 500, population 300, crossover rate 59%, mutation rate 5%, and elitism rate 4%; and 5) For CA-based LCLU predictive modeling, the recommended configuration of CA components is space 6025 * 12993, state 2, von Neumann neighborhood 3 * 3, time step 2-3 years, and optimized transition rules

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

Towards simulating the emergence of environmentally responsible behavior among natural resource users : an integration of complex systems theory, machine learning and geographic information science

La gouvernance pour le développement durable comporte de nombreux défis. L'un de ces défis consiste à mieux comprendre les systèmes socio-écologiques gouvernés. Dans de tels systèmes, l'apprentissage par essais et erreurs implique le risque de conséquences inattendues, irréversibles et néfastes. De plus, en raison de la complexité des systèmes socio-écologiques, les leçons tirées d'expériences à petite échelle ne peuvent pas toujours être applicables à des problèmes à grande échelle. Un autre aspect difficile des problèmes de développement durable est que ces problèmes sont souvent multidisciplinaires et composés de composants qui sont chacun étudiés individuellement dans une discipline différente, mais il existe peu d'informations sur leur comportement ensemble. Un troisième défi de la gouvernance pour le développement durable est qu'il est souvent nécessaire d'impliquer les parties prenantes dans des actions de gestion et des mesures d'intervention coûteuses pour les individus qui y participent. De plus, dans de nombreuses situations de ce type, les incitations financières et l'application des réglementations se soldent par un échec et ne constituent donc pas des options de gouvernance. Dans cette thèse, les défis ci-dessus sont abordés dans un exemple de contrôle des perturbations forestières avec une approche intégrée. Pour éviter le problème des effets indésirables irréversibles et pour permettre des expériences répétées, une approche de simulation est utilisée. Pour relever le défi de la multidisciplinarité des problèmes des systèmes socio-écologiques, deux modèles sont développés indépendamment - portant sur les aspects sociaux et écologiques du système de l'étude - et ils sont ensuite couplés de telle sorte que la sortie de chaque modèle est utilisée comme entrée pour l'autre modèle. Pour résoudre le problème de l'engagement des parties prenantes, un plan est proposé pour la promotion d'un comportement respectueux de l'environnement. Ce plan est basé sur l'offre de reconnaissance à ceux qui adoptent volontairement le comportement responsable. Le modèle écologique de cette étude, qui simule la propagation d'une perturbation forestière, est construit à l'aide de l’apprentissage automatique supervisé. Le modèle social de cette étude, qui simule l'émergence d'une nouvelle norme de comportement, est construit à l'aide de l'apprentissage par renforcement. Les deux modèles sont testés et validés avant couplage. Le modèle couplé est ensuite utilisé comme un laboratoire virtuel, où plusieurs expériences sont réalisées dans un cadre hypothétique et selon différents scénarios. Chacune de ces expériences est une simulation. A travers ces simulations, cette étude montre qu'avec un algorithme de prise de décision approprié et avec suffisamment de temps pour l'interaction entre une entité gouvernante et la société, il est possible de créer une motivation pour un comportement responsable dans la société. En d'autres termes, il est possible d'encourager la participation volontaire des acteurs à l'action pour le développement durable, sans que l'entité gouvernante ait besoin d'utiliser des incitations financières ou d'imposer son autorité. Ces résultats peuvent être applicables à d'autres contextes où un comportement responsable des individus ou des entreprises est recherché afin d'atténuer l'impact d'une perturbation, de protéger une ressource écologique, ou de faciliter une transition sectorielle vers la durabilité.Governance for sustainable development involves many challenges. One of those challenges is to gain insight about the social-ecological systems being governned. In such systems, learning by trial and error involve the risk of unexpected, irreversible and adverse consequences. Moreover, due to complexity of social-ecological systems, lessons learned from small scale experiments may not be applicable in large-scale problems. Another challenging aspect of problems of sustainable development is that these problems are often multidisciplinary and comprised of components that are each studied individually in a different discipline, but little information exists about their behavior together as a whole. A third challenge in governance for sustainable development is that often it is necessary to involve stakeholders in management actions and intervention measures that are costly for individuals who participate in them. Moreover, in many of these situations financial incentives or enforcement of regulations result in failure, and are thus not options for governance. In this thesis, the above challenges are addressed in an example case of forest disturbance control with an integrated approach. To avoid the problem of irreversible adverse effects and to allow repeated experiments, a simulation approach is used. To tackle the challenge of multidisciplinarity of problems of social-ecological systems, two models are independently developed – pertaining to social and ecological aspects of the system of the study – and they are subsequently coupled in such a way that the output of each model served as an input for the other. To address the problem of engagement of stakeholders, a scheme is proposed for promotion of environmentally responsible behavior. This scheme is based on offering recognition to those who voluntarily perform the responsible behavior. The ecological model of this study, which simulates the spread of a forest disturbance, is built using Supervised Machine Learning. The social model of this study, which simulates the emergence of a new norm of behavior, is built using Reinforcement Learning. Both models are tested and validated before coupling. The coupled model is then used as a virtual laboratory, where several experiments are performed in a hypothetical setting and under various scenarios. Each such experiment is a simulation. Through these simulations, this study shows that with an appropriate decision-making algorithm and with sufficient time for interaction between a governing entity and the society, it is possible to create motivation for responsible behavior in the society. In other words, it is possible to encourage voluntary participation of stakeholders in action for sustainable development, without the need for the governing entity to use financial incentives or impose its authority. These results may be applicable to other contexts where responsible behavior by individuals or enterprises is sought in order to mitigate the impact of a disturbance, protect an ecological resource, or facilitate a sectoral transition towards sustainability

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning